Enabling Intelligent IoTs for Histopathology Image Analysis Using Convolutional Neural Networks.

Area of Science:

• Digital Pathology
• Medical Imaging AI
• Computational Pathology

Background:

• Histopathology image analysis is crucial for cancer diagnosis but time-consuming for pathologists.
• Convolutional Neural Networks (CNNs) show promise but require significant computational power and time.
• Optimizing CNNs for efficiency is vital for widespread healthcare applications.

Purpose of the Study:

• To implement a quantized ResNet model for optimizing inference power consumption in histopathology image analysis.
• To evaluate the trade-offs between classification accuracy, energy consumption, and hardware utilization.
• To assess the feasibility of using compressed and lower bit-width models for medical imaging.

Main Methods:

• Implemented a quantized ResNet model on histopathology image datasets.
• Applied compression techniques including channel reduction and sparsity.
• Trained the model using original RGB images and then evaluated optimized representations.
• Analyzed classification accuracy, energy estimation, and hardware utilization metrics.

Main Results:

• Achieved a 6% accuracy increase with sparsity on RGB images at lower bit-widths (<8:8>) compared to the 32-bit baseline.
• Demonstrated considerably lower energy consumption for lower bit-width and compressed color modes versus standard RGB 32-bit.
• Observed higher resource utilization and a reduced memory bottleneck ratio with lower bit-width implementations.

Conclusions:

• Quantized ResNet models with compression offer a viable solution for efficient histopathology image analysis.
• Optimized models reduce power consumption and improve resource utilization, making them suitable for energy-limited devices.
• This approach supports the integration of AI in Internet of Things (IoT) healthcare systems.